#FULL Tensorboard, python ~/tensorflow/tensorflow/tensorboard/tensorboard.py  --logdir=/home/chen/learn_tensorflow/logs



import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

def add_layer(inputs,in_size,out_size,n_layer,activation_function=None):
    layer_name='layer%s'%n_layer
    with tf.name_scope(layer_name):
      with tf.name_scope('weights'):
        Weights=tf.Variable(tf.random_normal([in_size,out_size]),name='W')
        tf.histogram_summary(layer_name+'/weights',Weights)
      with tf.name_scope('bias'):
        biases=tf.Variable(tf.zeros([1,out_size])+0.1) 
        #biases is recommended not 0
        tf.histogram_summary(layer_name+'/biases',biases)
      with tf.name_scope('Wx_plus_b'):
        Wx_plus_b=tf.matmul(inputs,Weights)+biases
      if activation_function is None:
        outputs=Wx_plus_b
      else:
        outputs=activation_function(Wx_plus_b)
        tf.histogram_summary(layer_name+'/outputs',outputs)
      return outputs
    
#Make up some real data
x_data=np.linspace(-1,1,300)[:,np.newaxis]
noise=np.random.normal(0,0.05,x_data.shape)
y_data=np.square(x_data)-0.5+noise

with tf.name_scope('inputs'):
  xs=tf.placeholder(tf.float32,[None,1],name='x_input')
  ys=tf.placeholder(tf.float32,[None,1],name='y_input')

#add hidden layer
l1=add_layer(xs,1,10,n_layer=1,activation_function=tf.nn.relu)
#add input layer
prediction=add_layer(l1,10,1,n_layer=2,activation_function=None)

#the error between prediction and real data
with tf.name_scope('loss'):
  loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))
  tf.scalar_summary('loss',loss)



with tf.name_scope('train'):
  train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)


sess=tf.Session()
merged=tf.merge_all_summaries()
writer=tf.train.SummaryWriter("logs/",sess.graph)

sess.run(tf.initialize_all_variables())


for i in range(1000):
  sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
  if i%50==0:
     result=sess.run(merged,feed_dict={xs:x_data,ys:y_data})
     writer.add_summary(result,i)



